Various professions often look for patterns in large amounts of data that relates to real life issues. For example, one who studies medicine might look for patterns that indicate a correlation between two physical properties of a patient. Particularly, analyzing large sets of patient data might indicate that individuals who share certain characteristics and past experiences are at risk for particular types of illnesses or other adverse conditions. Finding these patterns in the data provides scientists with additional tools that can help discover causal relationships and thereby find ways to treat such illnesses.
Various techniques such as factor analysis and Principal Component Analysis (PCA) can be used to reduce a number of observable variables within a set of data to a smaller number of unobserved variables that affect the observable variables. Viewing these unobserved variables helps to find patterns within the data. However, such techniques only find patterns that are present within most of the data. In some cases, a pattern may exist within a smaller percentage of the data. Such a pattern would not be picked up by various factor analysis methods.
Throughout the drawings, identical reference numbers designate similar, but not necessarily identical, elements.